sccloud.calc_kBET

sccloud.calc_kBET(data, attr, rep='pca', K=25, alpha=0.05, n_jobs=-1, random_state=0, temp_folder=None)[source]

Calculate the kBET metric of the data w.r.t. a specific sample attribute and embedding.

This kBET metric is based on paper “A test metric for assessing single-cell RNA-seq batch correction” [Büttner18] in Nature Methods, 2018.

Parameters

  • data (anndata.AnnData) – Annotated data matrix with rows for cells and columns for genes.

  • attr (str) – The sample attribute to consider. Must exist in data.obs.

  • rep (str, optional, default: "pca") – The embedding representation to be used. The key 'X_' + rep must exist in data.obsm. By default, use PCA coordinates.

  • K (int, optional, default: 25) – Number of nearest neighbors, using L2 metric.

  • alpha (float, optional, default: 0.05) – Acceptance rate threshold. A cell is accepted if its kBET p-value is greater than or equal to alpha.

  • n_jobs (int, optional, default: -1) – Number of threads used. If -1, use all available threads.

  • random_state (int, optional, default: 0) – Random seed set for reproducing results.

  • temp_folder (str, optional, default: None) – Temporary folder for joblib execution.

Return type

Tuple[float, float, float]

Returns

  • stat_mean (float) – Mean kBET chi-square statistic over all cells.

  • pvalue_mean (float) – Mean kBET p-value over all cells.

  • accept_rate (float) – kBET Acceptance rate of the sample.

Examples

>>> scc.calc_kBET(adata, attr = 'Channel')

>>> scc.calc_kBET(adata, attr = 'Channel', rep = 'umap')